Job Title: Machie Learning Engineer

Salary: $140,000 - $170,000

Location: Washington D.C (Hybrid/Remote)

My client is an expanding start-up who are pioneering the future of space weather intelligence. Their platform leverages cutting-edge science and advanced machine learning to create fully integrated solutions which enhance resilience and mitigate risks from the space environment.

They are currently seeking a talented Machine Learning Engineer to join their team and help develop ML models that turn complex data into actionable insights, driving the next generation of space-tech applications.

Key Responsibilities:

Design and deploy machine learning models to analyze and interpret physics-based data, especially in the areas of space weather, satellite telemetry, and atmospheric dynamics.
Implement numerical modeling techniques to simulate physical systems, integrating these with ML approaches for enhanced predictive accuracy.
Collaborate with cross-functional teams to understand project requirements and to translate complex, physics-based processes into ML solutions.
Optimize model performance and scalability for deployment on cloud platforms (AWS).
Implement data preprocessing, feature engineering, and data augmentation techniques to improve model accuracy.
Build, maintain, and improve data pipelines, ensuring the seamless flow of data from ingestion to deployment.
Monitor and evaluate model performance post-deployment, making updates as needed for continuous improvement.
Ensure models adhere to security, privacy, and regulatory standards.

Qualifications:

Proven experience in developing and deploying machine learning models using Keras, TensorFlow, PyTorch, Jax, or similar modern frameworks.
Experience building numerical and ML models of physics-based systems with exposure to large datasets or distributed systems.
Strong background in data science, including experience with data preprocessing, feature engineering, and model evaluation.
Proficiency in cloud platforms (AWS) for deploying and scaling machine learning models.
Familiarity with containerization tools like Docker for model deployment.
Solid understanding of statistical methods, algorithms, and performance metrics used in machine learning.
Strong problem-solving and communication skills, and the ability to work collaboratively in a fast-paced environment.

Preferred Qualifications:

Background in physics, atmospheric science, aerospace, electrical engineering, or a related field
Experience building Physics-Informed ML models (PINN, DeepOnet, FNO/AFNO) using frameworks such as DeepXDE or Modulus
Knowledge of MLOps practices, including CI/CD for ML, model versioning, and automated monitoring. Experience putting ML models into production.
Relevant certifications in cloud platforms or machine learning frameworks.
Experience with real-time data processing (Spark, Flink, Dataflow, Kafka, Pulsar, etc.)
Experience debugging and maintaining live production systems on Kubernetes.

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